Abstract
Text detection in natural scene images is important for content-based image analysis. It consists of different component analysis for text detection video frames. Multioriented text detection in video frames is one of the text detection in videos frames. Multi-oriented text detection is not easy as detection of captions or graphics which is usually appears in horizontal direction and has high contrast compared to its background. Multi-oriented text detection generally refers to scene text that makes text detection more challenging. Therefore conventional text detection may not give good results for multi-oriented video frames text detection. Hence, in this paper, we present a new enhancement method that includes the product of Laplacian and Sobel operations to enhance text pixels in videos. To classify true text pixels, we propose a Bayesian classifier without assuming a priori probability about the input frame but estimating it based on three probable matrices. Three different ways of clustering are performed on the output of the enhancement method to obtain the three probable matrices. Text candidates are obtained by intersecting the output of the Bayesian classifier with the Canny edge map of the input frame. The robustness of the methods are analyzed our own datasets under different video frames. Finally, it coarse-to-fine detection locates text regions efficiently.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.